城市固废焚烧过程烟气含氧量自适应预测控制

Adaptive Predictive Control of Oxygen Content in Flue Gas for Municipal Solid Waste Incineration Process

在城市固体废弃物焚烧(Municipal solid waste incineration,MSWI)过程中,烟气含氧量是影响焚烧效果的重要工艺参数.由于固废焚烧过程的复杂性,在实际应用过程中,难以实现烟气含氧量的有效控制.面向城市固废焚烧过程烟气含氧量控制的实际需求,提出一种基于数据驱动的烟气含氧量自适应预测控制方法.首先,采用自适应模糊C均值(Fuzzy C-means,FCM)算法辅助确定径向基函数(Radial basis function,RBF)神经网络隐含层神经元个数及初始中心,建立基于FCM算法的径向基函数神经网络预测模型,并在控制过程中通过自适应更新策略在线调节预测模型参数;然后,利用梯度下降算法求解控制律,并基于李雅普诺夫理论分析了所提控制方法的稳定性;最后,基于城市固废焚烧厂实际数据,验证了所提控制方法的有效性.

Oxygen content in flue gas is an important process parameter for incineration efficiency in municipal solid waste incineration(MSWI).Due to the complexity of municipal solid waste incineration process,it is difficult to achieve effective control of oxygen content in flue gas in practical application.A data-driven adaptive predictive control method for oxygen content in flue gas in MSWI process is proposed in this paper.Firstly,an adaptive fuzzy C-means(FCM)algorithm is used to determine the number of hidden layer neurons and the initial clustering center of radial basis function(RBF)neural network model,and the radial basis function neural network prediction model based on FCM algorithm is established.During the control process,the prediction model parameters are adjusted adaptively by an online updating strategy.Then,the gradient descent method is exploited to solve the control law,and the stability of the control system is analyzed based on the Lyapunov theory.Finally,the effectiveness of the proposed control method is verified based on the actual data of the municipal solid waste incineration plant.